Merge branch 'master' into devel

* master: (123 commits)
  Fix non-linear path factors
  Bump verison number
  Set some examples to not run to save check time
  Initalise scDatEx to avoid NOTE
  Tidy up and bump version
  Replace 1:x
  Remove splatter.Rproj
  Add more detail about counts matrix
  Add installation to vignette
  Set R_TESTS environment
  Bump version to start build
  splatter 0.99.3
  Updata scDDEstimate
  Add condition parameter
  Modify SCDDParams to take SummarizedExperiment
  Add logo to vignettes directory
  Remove scDD github remote
  Update to R-devel
  Address Bioconductor build warnings, notes
  Prepare for Bioconductor submission
  ...

From: Luke Zappia <lazappi@users.noreply.github.com>

git-svn-id: https://hedgehog.fhcrc.org/bioconductor/trunk/madman/Rpacks/splatter@126081 bc3139a8-67e5-0310-9ffc-ced21a209358

DESCRIPTION

 Package: splatter
 Type: Package
 Title: Simple Simulation of Single-cell RNA Sequencing Data
-Version: 0.99.7
-Date: 2016-12-29
+Version: 0.99.8
+Date: 2017-01-23
 Author: Luke Zappia
-Authors@R: c(person("Luke", "Zappia", role = c("aut", "cre"), email =
-        "luke.zappia@mcri.edu.au"), person("Belinda", "Phipson", role =
-        c("aut"), email = "belinda.phipson@mcri.edu.au"),
-        person("Alicia", "Oshlack", role = c("aut"), email =
-        "alicia.oshlack@mcri.edu.au"))
+Authors@R:
+    c(person("Luke", "Zappia", role = c("aut", "cre"),
+      email = "luke.zappia@mcri.edu.au"),
+      person("Belinda", "Phipson", role = c("aut"),
+      email = "belinda.phipson@mcri.edu.au"),
+      person("Alicia", "Oshlack", role = c("aut"),
+      email = "alicia.oshlack@mcri.edu.au"))
 Maintainer: Luke Zappia <luke.zappia@mcri.edu.au>
-Description: Splatter is a package for the simulation of single-cell
-        RNA sequencing count data. It provides a simple interface for
-        creating complex simulations that are reproducible and
-        well-documented.
+Description: Splatter is a package for the simulation of single-cell RNA
+    sequencing count data. It provides a simple interface for creating complex
+    simulations that are reproducible and well-documented.
 License: GPL-3 + file LICENSE
 LazyData: TRUE
-Depends: R (>= 3.4), scater
-Imports: fitdistrplus, edgeR, stats, locfit, akima, Biobase, checkmate,
-        methods, utils, matrixStats, ggplot2, scales
-Suggests: testthat, scran, progress, lme4, pscl, scDD, knitr,
-        rmarkdown, BiocStyle, covr, S4Vectors, SummarizedExperiment
-biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression,
-        Sequencing, Software
+Depends:
+    R (>= 3.4),
+    scater
+Imports:
+    fitdistrplus,
+    edgeR,
+    stats,
+    locfit,
+    akima,
+    Biobase,
+    checkmate,
+    methods,
+    utils,
+    matrixStats,
+    ggplot2,
+    scales
+Suggests:
+    testthat,
+    scran,
+    progress,
+    lme4,
+    pscl,
+    scDD,
+    knitr,
+    rmarkdown,
+    BiocStyle,
+    covr,
+    S4Vectors,
+    SummarizedExperiment
+biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing,
+    Software
 URL: https://github.com/Oshlack/splatter
 BugReports: https://github.com/Oshlack/splatter/issues
 RoxygenNote: 5.0.1

NEWS.md

+# 0.99.8
+
+* Fix bug that meant non-linear path factors weren't stored in output
+
 # 0.99.7
 
 * Small changes to avoid NOTEs and reduce check time

R/splat-simulate.R

@@ -53,27 +53,29 @@
 #'         \describe{
 #'             \item{Gene}{Unique gene identifier.}
 #'             \item{BaseGeneMean}{The base expression level for that gene.}
-#'             \item{OutlierFactor}{Expression outlier factor for that gene. Values
-#'             of 1 indicate the gene is not an expression outlier.}
+#'             \item{OutlierFactor}{Expression outlier factor for that gene.
+#'             Values of 1 indicate the gene is not an expression outlier.}
 #'             \item{GeneMean}{Expression level after applying outlier factors.}
-#'             \item{DEFac[Group]}{The differential expression factor for each gene
-#'             in a particular group. Values of 1 indicate the gene is not
+#'             \item{DEFac[Group]}{The differential expression factor for each
+#'             gene in a particular group. Values of 1 indicate the gene is not
 #'             differentially expressed.}
 #'             \item{GeneMean[Group]}{Expression level of a gene in a particular
 #'             group after applying differential expression factors.}
+#'             \item{SigmaFac[Path]}{Factor applied to genes that have
+#'             non-linear changes in expression along a path.}
 #'         }
 #'     }
 #'     \item{\code{assayData}}{
 #'         \describe{
-#'             \item{BaseCellMeans}{The expression of genes in each cell adjusted for
-#'             expected library size.}
-#'             \item{BCV}{The Biological Coefficient of Variation for each gene in
-#'             each cell.}
-#'             \item{CellMeans}{The expression level of genes in each cell adjusted
-#'             for BCV.}
+#'             \item{BaseCellMeans}{The expression of genes in each cell
+#'             adjusted for expected library size.}
+#'             \item{BCV}{The Biological Coefficient of Variation for each gene
+#'             in each cell.}
+#'             \item{CellMeans}{The expression level of genes in each cell
+#'             adjusted for BCV.}
 #'             \item{TrueCounts}{The simulated counts before dropout.}
-#'             \item{Dropout}{Logical matrix showing which values have been dropped
-#'             in which cells.}
+#'             \item{Dropout}{Logical matrix showing which values have been
+#'             dropped in which cells.}
 #'         }
 #'     }
 #' }
@@ -449,6 +451,15 @@ splatSimPathCellMeans <- function(sim, params) {
     group.names <- unique(groups)
     exp.lib.sizes <- pData(sim)$ExpLibSize
 
+    # Generate non-linear path factors
+    for (idx in seq_along(path.from)) {
+        # Select genes to follow a non-linear path
+        is.nonlinear <- as.logical(rbinom(nGenes, 1, path.nonlinearProb))
+        sigma.facs <- rep(0, nGenes)
+        sigma.facs[is.nonlinear] <- path.sigmaFac
+        fData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
+    }
+
     # Generate paths. Each path is a matrix with path.length columns and
     # nGenes rows where the expression from each genes changes along the path.
     path.steps <- lapply(seq_along(path.from), function(idx) {
@@ -462,16 +473,13 @@ splatSimPathCellMeans <- function(sim, params) {
         # Find the means at the end position
         means.end <- fData(sim)[[paste0("GeneMeanPath", idx)]]
 
-        # Select genes to follow a non-linear path
-        is.nonlinear <- as.logical(rbinom(nGenes, 1, path.nonlinearProb))
-        sigma.facs <- rep(0, nGenes)
-        sigma.facs[is.nonlinear] <- path.sigmaFac
+        # Get the non-linear factors
+        sigma.facs <- fData(sim)[[paste0("SigmaFacPath", idx)]]
+
         # Build Brownian bridges from start to end
         steps <- buildBridges(means.start, means.end, n = path.length[idx],
                               sigma.fac = sigma.facs)
 
-        fData(sim)[[paste0("SigmaFacPath", idx)]] <- sigma.facs
-
         return(t(steps))
     })
 

man/splatSimulate.Rd

@@ -78,27 +78,29 @@ slots. This additional information includes:
         \describe{
             \item{Gene}{Unique gene identifier.}
             \item{BaseGeneMean}{The base expression level for that gene.}
-            \item{OutlierFactor}{Expression outlier factor for that gene. Values
-            of 1 indicate the gene is not an expression outlier.}
+            \item{OutlierFactor}{Expression outlier factor for that gene.
+            Values of 1 indicate the gene is not an expression outlier.}
             \item{GeneMean}{Expression level after applying outlier factors.}
-            \item{DEFac[Group]}{The differential expression factor for each gene
-            in a particular group. Values of 1 indicate the gene is not
+            \item{DEFac[Group]}{The differential expression factor for each
+            gene in a particular group. Values of 1 indicate the gene is not
             differentially expressed.}
             \item{GeneMean[Group]}{Expression level of a gene in a particular
             group after applying differential expression factors.}
+            \item{SigmaFac[Path]}{Factor applied to genes that have
+            non-linear changes in expression along a path.}
         }
     }
     \item{\code{assayData}}{
         \describe{
-            \item{BaseCellMeans}{The expression of genes in each cell adjusted for
-            expected library size.}
-            \item{BCV}{The Biological Coefficient of Variation for each gene in
-            each cell.}
-            \item{CellMeans}{The expression level of genes in each cell adjusted
-            for BCV.}
+            \item{BaseCellMeans}{The expression of genes in each cell
+            adjusted for expected library size.}
+            \item{BCV}{The Biological Coefficient of Variation for each gene
+            in each cell.}
+            \item{CellMeans}{The expression level of genes in each cell
+            adjusted for BCV.}
             \item{TrueCounts}{The simulated counts before dropout.}
-            \item{Dropout}{Logical matrix showing which values have been dropped
-            in which cells.}
+            \item{Dropout}{Logical matrix showing which values have been
+            dropped in which cells.}
         }
     }
 }